Data Quality Developer/Analyst

A DG Academy Early-Career, Role-Based Bootcamp

What Is This Role?

A Data Quality Developer/Analyst ensures data accuracy, consistency, and completeness for an organization. Tasks include analyzing data, designing data cleansing processes, creating documentation, collaborating with teams, implementing data governance policies, and evaluating data quality tools.

The Job In Real Life

As a Data Quality Developer/Analyst, you will play a key role in ensuring the integrity and reliability of the data being used within your organization. Your responsibilities will include analyzing data to identify patterns and anomalies, designing and implementing processes to clean and validate data, and collaborating with other teams to resolve data issues. You will also be responsible for creating and maintaining data dictionaries and other documentation, as well as implementing data governance policies and procedures to ensure the proper management and security of data.

In addition to these technical tasks, you will also be responsible for working with stakeholders across the organization to understand their data needs and ensure that the data being used meets their requirements. You may be involved in data migration and integration projects, and will need to be able to effectively communicate with different teams and departments to coordinate these efforts.

To support your work, you may be responsible for evaluating and implementing data quality tools and technologies that can automate and streamline data cleansing and validation processes. You may also be called upon to develop custom solutions to meet the specific data quality needs of your organization.

Overall, as a Data Quality Developer/Analyst, you will be a key member of the data management team, helping to ensure the reliability and integrity of the data being used within the organization and supporting the delivery of accurate and useful insights to stakeholders.

Leading Technologies

As a Data Quality Developer/Analyst, you will have the opportunity to be certified in one of the following technologies as part of your bootcamp.

Our culture is defined around our people’s excellence, growth, progress, and continuity. We unfold full potentials, switch it on, and carefully cultivate it in a warm, unique, engaging and enthusiastic environment to enhance creativities and reflects long-held values of inclusion and diversity.

We cultivate a skilled and thriving workforce in an innovative environment to inspire exceptional performances and significant impacts on next-generation technology.

Collibra Data Quality and Observability

During our 16 weeks tailored training program you will be put on track to a rewarding career by experiencing real-world project and gaining the most valuable knowledge before transiting to our partners.

Informatica Cloud Data Quality and Data Integration

Ataccama ONE Data Quality and Data Integration

What The Bootcamp Covers

Data analysis: A Data Quality Developer/Analyst will often spend a significant amount of time analyzing data to identify patterns and anomalies. This may involve using SQL or other programming languages to run queries and extract data from databases or other data sources.

Data cleansing and validation: Once data issues have been identified, the Data Quality Developer/Analyst will be responsible for designing and implementing processes to clean and validate the data. This may involve writing scripts or code to automate data cleansing and validation tasks, as well as manually reviewing and correcting data as needed.

Documentation: It is important for a Data Quality Developer/Analyst to keep detailed documentation of data cleansing and validation processes, as well as any other information related to the data being used within the organization. This may include creating and maintaining data dictionaries, data flow diagrams, and other documentation.

Collaboration: A Data Quality Developer/Analyst will often work closely with other teams within the organization, such as IT, business intelligence, and data engineering, to identify and resolve data issues. They may also be involved in data migration and integration projects, and will need to be able to effectively communicate with stakeholders to understand their data needs and ensure that the data being used meets their requirements.

Data governance: A Data Quality Developer/Analyst may also be responsible for implementing and enforcing data governance policies and procedures within the organization. This may include establishing processes for managing and securing data, as well as setting standards for data quality and ensuring that these standards are met.

Tools and technologies: Finally, a Data Quality Developer/Analyst may be responsible for evaluating and implementing data quality tools and technologies to support the organization's data management efforts. This may include selecting and configuring software or other tools to automate data cleansing and validation tasks, or developing custom solutions to meet the organization's specific data quality needs.

Bootcamp

The bootcamp training program for a Data Quality Developer/Analyst role will cover the key skills and knowledge needed to succeed in this role. Participants will learn about data analysis, data cleansing and validation, data integration and migration, data governance and documentation, and project management and communication. The program will also include a capstone project in which participants will apply their newly acquired skills to a real-world data quality challenge. By the end of the program, participants will have a strong foundation in the key concepts and techniques needed to be a successful Data Quality Developer/Analyst.

  • This module would cover the basics of data quality and data governance, including concepts such as data accuracy, completeness, consistency, and security. It could also introduce key stakeholders and their roles in data management within an organization.

  • This module would cover the basics of data analysis and visualization, including using SQL and other programming languages to extract and manipulate data from databases and other data sources. It could also cover tools and techniques for visualizing data, such as using charts and graphs to identify patterns and trends.

  • This module would cover best practices and techniques for cleansing and validating data, including using scripts and code to automate data cleansing tasks and manually reviewing and correcting data as needed. It could also cover the use of data quality tools and technologies to support these efforts.

  • This module would cover the basics of data integration and migration, including common challenges and best practices for moving data between different systems and formats. It could also cover techniques for handling data transformation and mapping, as well as tools and technologies that can support these efforts.

  • This module will cover the importance of data governance and the role of data quality in supporting good governance practices. It could also cover best practices for creating and maintaining data dictionaries and other documentation, as well as techniques for managing and securing data.

  • This module will cover skills and techniques for effectively managing data quality projects, including working with cross-functional teams and communicating with stakeholders. It could also cover techniques for setting and tracking project goals and metrics, as well as tools and technologies that can support project management efforts.

  • To bring everything together, the training program could conclude with a capstone project in which participants apply their newly acquired skills to a real-world data quality challenge. This could involve working in teams to analyze, clean, and validate data, and then presenting their findings and recommendations to the class.

  • The technology certification module will focus on teaching participants how to use specific tools and technologies related to data quality and data governance.

    This will include hands-on training and exercises using software and other tools for data analysis, data cleansing and validation, data integration and migration, and data governance.

    Depending on the specific LEADING TECHNOLOGY being covered, the module might include topics such as:

    SQL and other programming languages for data manipulation and analysis

    Data visualization tools and techniques

    Data quality software and platforms for automating data cleansing and validation tasks

    Data integration and migration tools and technologies

    Data governance platforms and tools for managing and securing data

    By the end of the technology certification module, participants will have a good understanding of how to use these tools and technologies to support their work as a Data Quality Developer/Analyst. They will also receive a certification or other formal recognition of their skills in the chosen leading technology.